An Approach to Carbon Emissions Prediction Using Generalized Regression Neural Network Improved by Genetic Algorithm

Abstract

The study on scientific analysis and prediction of China’s future carbon

emissions is conducive to balancing the relationship between economic

development and carbon emissions in the new era, and actively responding

to climate change policy. Through the analysis of the application of the

generalized regression neural network (GRNN) in prediction, this paper

improved the prediction method of GRNN. Genetic algorithm (GA) was

adopted to search the optimal smooth factor as the only factor of GRNN,

which was then used for prediction in GRNN. During the prediction of car

bon dioxide emissions using the improved method, the increments of data

were taken into account. The target values were obtained after the calcula

tion of the predicted results. Finally, compared with the results of GRNN,

the improved method realized higher prediction accuracy. It thus offers a

new way of predicting total carbon dioxide emissions, and the prediction

results can provide macroscopic guidance and decision-making reference

for China’s environmental protection and trading of carbon emissions.